5 Pandas Groupby Tricks to Know in Python

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class="region-inner"><div id="block-blognationalheader" class="block block-bix-blogs block-bix-blogs-header-national"><div class="block-content with-hero-image with-byline-background"><div class="header-left"/><div class="background"><div class="header-right"><div class="tags"><div class="tags-inner"><a href="https://builtin.com/data-science" target="_blank">Data Science</a></div><div class="tags-inner"><a href="https://builtin.com/expert-contributors" target="_blank">Expert Contributors</a></div><div class="tags-inner tag-inner-counter">+4</div><div class="tags-inner-mobile"><a href="https://builtin.com/data-science" target="_blank">Data Science</a></div><div class="tag-inner-mobile-counter">+5</div></div><h1 class="title">5 Pandas Groupby Tricks to Know in Python</h1><div class="subtitle">In Pandas, groupby splits all the records from your data set into different categories or groups and offers you flexibility to analyze the data.</div><div class="written-by-container"><div class="writtenby_photo"><img class="b-lazy" src="https://builtin.com/cdn-cgi/image/f=auto,quality=80,width=48,height=48/https://builtin.com/sites/www.builtin.com/files/styles/64_x_64/public/2022-11/Suraj%20Gurav.jpeg"/></div><div class="written-info"><div class="writtenby_name">Written by<a href="/our-editorial-staff/suraj-gurav" hreflang="en">Suraj Gurav</a></div><div class="writtenby_date">Published on Jun. 27, 2023</div><div class="written-by-popup-box" id="written-by-popup-box"><div class="top"><div class="writtenby_photo"><img src="https://builtin.com/cdn-cgi/image/f=auto,quality=80,width=48,height=48/https://builtin.com/sites/www.builtin.com/files/styles/64_x_64/public/2022-11/Suraj%20Gurav.jpeg"/></div><div class="writtenby_name"><a href="/our-editorial-staff/suraj-gurav" hreflang="en">Suraj Gurav</a><div class="written_by_title">Analytics &amp; Media Manager at Amazon</div></div><a href="/our-editorial-staff/suraj-gurav"><svg xmlns="http://www.w3.org/2000/svg" viewbox="0 0 448 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class="svg-inline--fa" role="img" aria-hidden="true" style="color:currentColor"><path d="M279.14 288l14.22-92.66h-88.91v-60.13c0-25.35 12.42-50.06 52.24-50.06h40.42V6.26S260.43 0 225.36 0c-73.22 0-121.08 44.38-121.08 124.72v70.62H22.89V288h81.39v224h100.17V288z" fill="currentColor"/></svg> </a></div></div></div><div class="node--content"><div class="clearfix text-formatted field field--name-body field--type-text-with-summary field--label-hidden field__item"><a href="https://builtin.com/data-science/pandas" target="_blank">Pandas</a> is a widely used <a href="https://builtin.com/data-science/python-libraries-data-science" target="_blank">Python library</a> for <a href="https://builtin.com/data-science/types-of-data-analysis" target="_blank">data analytics</a> projects, but it isn’t always easy to analyze the data and get valuable insights from it. You first need to transform and aggregate the data in Pandas to better understand it. Enter Pandas <code>groupby</code>. Pandas <code>groupby</code> splits all the records from your data set into different categories or groups and offers you flexibility to <a href="https://builtin.com/data-science/pandas-filter" target="_blank">analyze the data</a> by these groups. It’s an extremely efficient and must know function in data analysis, which gives you interesting insights within a few seconds. And that’s why it usually comes up in data science job interviews. <div class="snippet-box snippet-box-html"><div><h2 class="title">What Is Pandas Groupby?</h2><div class="description">Pandas <code>groupby</code> splits all the records from your data set into different categories or groups so that you can analyze the data by these groups. When you use the <code>.groupby()</code> function on any categorical column of DataFrame, it returns a <code>GroupBy</code> object, which you can use other methods on to group the data. </div></div></div>In this article, I’ll explain five easy Pandas <code>groupby</code> tricks with examples to help you perform data analysis efficiently and ace your next <a href="https://builtin.com/data-science/data-science-interview-questions" target="_blank">data science interview</a>. Before going further, let’s understand the basics. <h2>How Does Pandas Groupby Work?</h2>When you use the <code>.groupby()</code> function on any categorical <a href="https://builtin.com/data-science/pandas-show-all-columns" target="_blank">column</a> of DataFrame, it returns a <code>GroupBy</code> object. Then you can use different methods on this object and even aggregate other columns to get the summary view of the data set.For example, you can use the .<code>groupby()</code> function on the column <code>“Product_Category”</code> in <code>df</code> to get the <code>GroupBy</code> object:<pre><code>df_group = df.groupby("Product_Category") type(df_group) # Output pandas.core.groupby.generic.DataFrameGroupBy</code></pre>The returned <code>GroupBy</code> object is nothing but a dictionary where keys are the unique groups in which records are split and values are the columns of each group that are not mentioned in <code>groupby</code>.The <code>GroupBy</code> object holds the contents of the entire DataFrame but in a more structured form. And just like <a href="https://builtin.com/software-engineering-perspectives/convert-list-to-dictionary-python" target="_blank">dictionaries</a> there are several methods to get the required data efficiently. Let’s start with importing your data into Pandas. <div class="video-embed-field-provider-youtube video-embed-field-responsive-video"><iframe width="854" height="480" loading="lazy" frameborder="0" allowfullscreen="allowfullscreen" data-src="https://www.youtube.com/embed/qy0fDqoMJx8?autoplay=0&amp;start=0&amp;rel=0" class="b-lazy"/><noscript><div class="player-unavailable"><div class="message">An error occurred.</div><div class="submessage">Unable to execute JavaScript. Try watching this video on <a href="https://www.youtube.com/embed/qy0fDqoMJx8?autoplay=0&amp;start=0&amp;rel=0" target="_blank">www.youtube.com</a>, or enable JavaScript if it is disabled in your browser.</div></div></noscript></div><figcaption class="video-caption">A tutorial on when to use Pandas groupby. | Video: Data School</figcaption>More on Pandas<a href="https://builtin.com/software-engineering-perspectives/pandas-iloc" target="_blank">Loc and iLoc Functions in Pandas Tutorial</a> <h2>5 Pandas Groupby Methods to Know</h2>For these examples, we’ll use a self-created “<a href="https://github.com/17rsuraj/data-curious/blob/master/TowardsDataScience/Dummy_Sales_Data_v1.csv" target="_blank">Dummy Sales Data</a>”, which you can get on my Github for free under MIT License.Let’s import the data set into Pandas DataFrame <code>df</code>.<pre><code>import pandas as pd df = pd.read_csv("Dummy_Sales_Data_v1.csv") df.head()</code></pre><figure><img alt="Dummy sales data" data-entity-type="file" data-entity-uuid="8e6e65e9-c2da-4d03-a61a-2c3948cf77fe" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" width="512" height="68" loading="lazy" data-src="https://builtin.com/sites/www.builtin.com/files/styles/ckeditor_optimize/public/inline-images/1_pandas-groupby.jpg" class=" b-lazy"/><figcaption>Dummy sales data | Image: Suraj Gurav</figcaption></figure>It’s a simple 9999 x 12 data set, which I created using Faker in Python. Now, let’s review some of the tricks you can do with <code>groupby</code> in Pandas. <h3>1. Number of Groups</h3>Once you split the data into different categories, it’s interesting to know how many different groups your data is now divided into.The simple and common answer is to use the <code>nunique()</code> function on any <a href="https://builtin.com/data-science/pandas-add-column" target="_blank">column</a>, which gives you a number of unique values in that column. As many unique values as there are in a column, the data will be divided into that many groups.For example, you can see how many unique groups can be formed using <code>Product_Category</code>.<pre><code>df.Product_Category.nunique() -- Output 5</code></pre>However, when you already have a <code>GroupBy</code> object, you can directly use the method <code>ngroups</code>, which gives you the answer you are looking for: <pre><code>df_group = df.groupby("Product_Category") df_group.ngroups -- Output 5</code></pre>Once you get the number of groups, you are still unaware about the size of each group. The next method gives you an idea of how large or small each group is. <h3>2. Group Sizes</h3>The number of rows in each group of <code>GroupBy</code> object can be easily obtained using the function <code>.size()</code>.For instance, let’s say you want to see how many different rows are available in each group of <code>“Product_Category”</code>:<pre><code>df.groupby("Product_Category").size()</code></pre><figure><img alt="Pandas group by Size " data-entity-type="file" data-entity-uuid="5b45efcf-70d6-43c8-bd8c-f80bfd1c755b" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" width="238" height="136" loading="lazy" data-src="https://builtin.com/sites/www.builtin.com/files/styles/ckeditor_optimize/public/inline-images/2_pandas-groupby.jpg" class=" b-lazy"/><figcaption>Pandas group by size. | Image: Suraj Gurav</figcaption></figure>The method simply counts the number of rows in each group. One may argue that the same results can be obtained using an aggregate function <code>count()</code>: <pre><code>df.groupby("Product_Category").count()</code></pre><figure><img alt="Pandas group by count" data-entity-type="file" data-entity-uuid="788cf07e-77ce-46d4-8030-e34b47c1dfcd" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" width="512" height="88" loading="lazy" data-src="https://builtin.com/sites/www.builtin.com/files/styles/ckeditor_optimize/public/inline-images/3_pandas-groupby.jpg" class=" b-lazy"/><figcaption>Pandas group by count. | Image: Suraj Gurav</figcaption></figure>You can see the numbers in both results are the same. However, there’s a significant difference in the way they are calculated.As per <a href="https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.count.html" target="_blank">Pandas</a>, the aggregate function <code>.count() </code>counts only the non-null values from each column, whereas <code>.size()</code> simply returns the number of rows available in each group irrespective of presence or absence of values.You need to have a strong understanding of the difference between these two functions before using them. If you want to see how many non-null values are present in each column of each group, use <code>.count()</code>. Otherwise, use <code>.size()</code>.Once you get the size of each group, you might want to take a look at the first, last or the record at any random position in the data. The next method can be handy in that case. <h3>3. Get First and Last</h3>These functions return the first and last records after data is split into different groups. Rather than referencing the index, it gives out the first or last row appearing in all the groups.For example, you can see the first record of in each group below:<pre><code>df.groupby("Product_Category").first()</code></pre><figure><img alt="See first row of each group in Pandas groupby" data-entity-type="file" data-entity-uuid="0b33ab5a-bb08-4e6e-a3d7-233d96d857b6" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" width="512" height="80" loading="lazy" data-src="https://builtin.com/sites/www.builtin.com/files/styles/ckeditor_optimize/public/inline-images/4_pandas-groupby.jpg" class=" b-lazy"/><figcaption>See first row of each group in Pandas groupby. | Image: Suraj Gurav</figcaption></figure>In a similar way, you can look at the last row in each group:<pre><code>df.groupby("Product_Category").last()</code></pre><figure><img alt="See last row of each group in Pandas groupby" data-entity-type="file" data-entity-uuid="1ecf54d4-82ee-4f13-9f91-2a0b6251e55d" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" width="512" height="79" loading="lazy" data-src="https://builtin.com/sites/www.builtin.com/files/styles/ckeditor_optimize/public/inline-images/5_pandas-groupby.jpg" class=" b-lazy"/><figcaption>See last row of each group in Pandas groupby. | Image: Suraj Gurav</figcaption></figure><code>.first()</code> and <code>.last()</code> returned the first and the last row once all the rows were grouped under each <code>Product_Category</code>.You can also extract a row at any other position, as well. For example, extracting the fourth row in each group is also possible using function <code>.nth()</code>.<pre><code>df.groupby("Product_Category").nth(3)</code></pre>Remember, indexing in <a href="https://builtin.com/software-engineering-perspectives/python" target="_blank">Python</a> starts with zero, therefore, when you say <code>.nth(3)</code>, you are actually accessing the fourth row.Logically, you can even get the first and last row using <code>.nth()</code> function. For example, you can get the first row in each group using <code>.nth(0)</code> and <code>.first()</code>, or the last row using <code>.nth(-1)</code> and <code>.last()</code>.So, why do these different functions even exist?Although <code>.first()</code> and <code>.nth(0)</code> can be used to get the first row, there is a difference in handling NaN or missing values. <code>.first()</code> give you first non-null values in each column, whereas <code>.nth(0)</code> returns the first row of the group, no matter what the values are. Same with <code>.last()</code>I recommend using <code>.nth()</code> over the other two functions to get the required row from a group, unless you are specifically looking for non-null records. But suppose, instead of retrieving only a first or a last row from the group, you might be curious to know the contents of a specific group. The next method quickly gives you that info. <h3>4. Get Groups</h3>The Pandas <code>GroupBy</code> method <code>get_group()</code> is used to select or extract only one group from the <code>GroupBy</code> object.For example, suppose you want to see the contents of <code>‘Healthcare’</code> group:<pre><code>df_group.get_group('Healthcare')</code></pre><figure><img alt="pandas group by get_group()" data-entity-type="file" data-entity-uuid="e1e5c4e9-221f-42f1-a458-ee7790528910" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" width="512" height="78" loading="lazy" data-src="https://builtin.com/sites/www.builtin.com/files/styles/ckeditor_optimize/public/inline-images/6_pandas-groupby.jpg" class=" b-lazy"/><figcaption>Pandas group by get_group() | Image: Suraj Gurav</figcaption></figure>As you see, there is no change in the structure of the data set, and you still get all the records where the product category is <code>‘Healthcare’</code>.I have an interesting use-case for this method: Slicing a DataFrame.Suppose, you want to select all the rows where <code>"Product Category"</code> is <code>‘Home’</code>. A simple and widely used method is to use bracket notation “[ ]”:<pre><code>df[df["Product_Category"]=='Home']</code></pre>Nothing is wrong with that, but you can get the exact same results with the method <code>.get_group()</code>:<pre><code>df_group = df.groupby("Product_Category") df_group.get_group('Home')</code></pre>To take it a step further, when you compare the performance between these two methods and run them 1,000 times each, <code>.get_group()</code> is more time-efficient. In fact, slicing with <code>.groupby()</code> is four times faster than with logical comparison.This method was about getting only a single group at a time by specifying the group name in the <code>.get_group()</code> method. But what if you want to have a look into the contents of all groups in one go?Remember,<code>GroupBy</code> object is a dictionary.So, you can iterate through it the same way as a dictionary — using key and value arguments. Here is how you can take a peek into the contents of each group:<pre><code>for name_of_group, contents_of_group in df_group: print(name_of_group) print(contents_of_group)</code></pre><figure><img alt="Iterate through each group of pandas groupby" data-entity-type="file" data-entity-uuid="00f54011-74a4-4920-8c5d-4642a8c2163a" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" width="512" height="84" loading="lazy" data-src="https://builtin.com/sites/www.builtin.com/files/styles/ckeditor_optimize/public/inline-images/7_pandas-groupby.jpg" class=" b-lazy"/><figcaption>Iterate through each group of pandas groupby. | Image: Suraj Gurav</figcaption></figure>This will list out the <a href="https://builtin.com/data-science/rename-columns-pandas" target="_blank">name</a> and contents of each group as shown above. Contents of only one group are visible in the picture, but in the Jupyter Notebook, you can see the same pattern for all the groups listed one below another.It basically shows you the first and last five rows in each group just like <code>.head()</code> and <code>.tail()</code> methods of Pandas DataFrame.The use of Pandas groupby is incomplete if you don’t aggregate the data. Let’s explore how you can use different aggregate functions on different columns in this last part. <h3>5. Aggregate Multiple Columns </h3>Applying an aggregate function on columns in each group is one of the most widely used practices to obtain a summary structure for further statistical analysis.And that is where Pandas <code>groupby</code> with aggregate functions is very useful. With <code>groupby</code>, you can split a data set into groups based on a single column or multiple columns and then apply aggregate functions on the remaining numerical columns.Let’s continue with the same example. After grouping the data by <code>product_category</code>, suppose you want to see what the average unit price and quantity in each product category is.All you need to do is refer to these columns in the <code>GroupBy</code> object using square brackets and apply the aggregate function <code>.mean()</code> on them:<pre><code>#Create a groupby object df_group = df.groupby("Product_Category") #Select only required columns df_columns = df_group[["UnitPrice(USD)","Quantity"]]